| Challenge: | Existing text-to-SQL parsers are often over-confident, thus casting doubt on their trustworthiness when deployed for real use. |
| Approach: | They propose a parser-independent error detection model for text-to-SQL semantic parsing . they use a language model of code as its bedrock and graph neural networks to learn structural features of queries . |
| Outcome: | The proposed model outperforms parser-dependent uncertainty metrics on three strong parsers . it could improve the performance and usability of text-to-SQL semantic parsing, it is shown . |
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Photon: A Robust Cross-Domain Text-to-SQL System (2020.acl-demos)
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Jichuan Zeng, Xi Victoria Lin, Steven C.H. Hoi, Richard Socher, Caiming Xiong, Michael Lyu, Irwin King
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